ram-lexsi/aligntune-testrun-RAFT
The ram-lexsi/aligntune-testrun-RAFT model is a 0.5 billion parameter causal language model developed by ram-lexsi, fine-tuned from Qwen/Qwen2.5-0.5B-Instruct. It utilizes the RAFT algorithm and TRL backend, built with the AlignTune framework. This model is designed for general text generation tasks, leveraging its compact size and 32768-token context length for efficient deployment.
Loading preview...
Overview
The ram-lexsi/aligntune-testrun-RAFT is a compact 0.5 billion parameter causal language model, fine-tuned from the Qwen/Qwen2.5-0.5B-Instruct base model. Developed by ram-lexsi, it is built using the AlignTune framework, which supports various open-source models, algorithms, and backends.
Key Characteristics
- Base Model: Fine-tuned from
Qwen/Qwen2.5-0.5B-Instruct. - Fine-tuning Algorithm: Employs the RAFT (Retrieval Augmented Fine-Tuning) algorithm.
- Backend: Utilizes the TRL (Transformer Reinforcement Learning) backend for training.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing longer inputs.
- Framework: Developed within the flexible AlignTune ecosystem, enabling broad compatibility and customization.
Usage
This model is suitable for integration into applications requiring a small yet capable language model, particularly for tasks where the RAFT algorithm's benefits in fine-tuning are advantageous. Its 0.5B parameters make it efficient for deployment in resource-constrained environments, while the large context window supports complex conversational or document-based applications.